We used qualitative and quantitative methods to evaluate the feasibility and acceptability of PA-monitoring wristbands and web-based technology by predominantly African-American, church-based populations in resource-limited Washington, D.C., neighborhoods. To address cardiovascular health in at-risk populations in Washington, D.C., we joined community leaders to establish a community advisory board, the D.C. Cardiovascular Health and Obesity Collaborative (D.C. CHOC). Our first initiative, the Washington, D.C., Cardiovascular Health and Needs Assessment, intends to evaluate cardiovascular health, social determinants of health and PA-monitoring technologies. At the recommendation of D.C. CHOC members, we conducted a focus group and piloted the proposed PA-monitoring system with community members representing churches that would be targeted by the Cardiovascular Health and Needs Assessment. Participants (n=8) agreed to wear a PA-monitoring wristband for two weeks and to log cardiovascular health factors on a secure online account. Wristbands collected accelerometer-based data that participants uploaded to a wireless hub at their church. Participants agreed to return after two weeks to participate in a moderated focus group to share experiences using this technology. Feasibility was measured by online account usage, wristband utilization and objective PA data. Acceptability was evaluated through thematic analysis of verbatim focus group transcripts. Focus group transcripts revealed that participants felt positively about incorporating the device into their church-based populations given improvements were made to device training, hub accessibility, and device feedback. PA-monitoring wristbands for objectively measuring PA appear to be a feasible and acceptable technology in Washington, D.C., resource-limited communities. User preferences include immediate device feedback, hands-on device training, explicit instructions, improved central hub accessibility, and designation of a church member as a trained point-of-contact. When implementing technology-based interventions in resource-limited communities, engaging the targeted community may aid in early identification of issues, suggestions and preferences. We also explored user characteristics of PA-tracking, wearable technology among the health and needs assessment population. Washington, D.C. Cardiovascular Health and Needs Assessment participants received a mobile health (mHealth) PA monitor and wirelessly uploaded PA data weekly to church data collection hubs. Participants (n=99) were 59+/-12 years, 79% female, 99% African-American, with a mean body mass index of 33.7 kg/m2. Eighty-one percent of participants uploaded PA data to the hub and were termed PA device users. Though PA device users were more likely to report lower household incomes, no differences existed between device users and non-users for device ownership or technology fluency. Findings suggest that mHealth systems with a wearable device and data-collection hub may feasibly target PA in resource-limited communities. Community-based behavioral interventions targeting cardiometabolic health in resource-limited communities should consider incorporation of wearable mHealth technology. Efforts to reduce barriers to using mHealth technology in resource-limited settings may aid in decreasing cardiometabolic health disparities in at-risk populations. We have begun to develop tools for assessing the relationship between the neighborhood built environment and health behaviors or outcomes for the target populations in the Washington DC Health and Needs Assessment. We evaluated a scoring method for virtual neighborhood audits utilizing the Active Neighborhood Checklist (the Checklist), a neighborhood audit measure, and assessed street segment representativeness in lower-income neighborhoods. Eighty-two home neighborhoods in the Washington, D.C. Cardiovascular Health/Needs Assessment were audited using Google Street View imagery and the Checklist (five sections with 89 total questions). Twelve street segments per home address were assessed for (1) Land-Use Type; (2) Public Transportation Availability; (3) Street Characteristics; (4) Environment Quality and (5) Sidewalks/Walking/Biking features. Checklist items were scored 0-2 points/question. A combinations algorithm was developed to assess street segments representativeness. Spearman correlations were calculated between built environment quality scores and Walk Score, a validated neighborhood walkability measure. Street segment quality scores ranged 10-47 (Mean = 29.4+/-6.9) and overall neighborhood quality scores, 172-475 (Mean = 352.3 +/- 63.6). Walk scores ranged 0-91 (Mean = 46.7 +/- 26.3). Street segment combinations correlation coefficients ranged 0.75-1.0. Significant positive correlations were found between overall neighborhood quality scores, four of the five Checklist subsection scores, and Walk Scores (r = 0.62, p < 0.001). This scoring method adequately captures neighborhood features in lower-income, residential areas and may aid in delineating impact of specific built environment features on health behaviors and outcomes. We also examined associations of perceived and objective neighborhood environment (NE) with sedentary time (ST) in the Washington, D.C. CV Health and Needs Assessment. Participants reported NE perceptions, including sidewalks, recreational areas, and crime presence. Factor analysis was conducted to explore pertinent constructs; factor sums were created and combined as Total Perception Score (TPS) (higher score=more favorable perception). Objective NE was assessed using Google Maps and the Active Neighborhood Checklist (ANC). ST was self-reported. Linear regression determined relationships between TPS and ST, and ANC scores and ST, for 1) overall population, 2) lower median-income D.C. areas, and 3) higher median-income DC and Maryland areas. For the sample (N=98.9% African-American, 78% female), lower median-income areas had significantly lower mean TPS and ANC scores than higher median-income areas (p<0.001). Three factors (neighborhood violence, physical/social environment, and social cohesion) were associated with overall NE perception. Among those in lower median-income areas, there was a negative association between TPS and ST that remained after covariate adjustment; this was not observed in higher median-income areas. There was no association between ANC scores and ST. Poorer NE perception is associated with greater ST for those in lower income areas, while objective environment is not related to ST. Multi-level interventions are needed to improve NE perceptions in lower-median income areas, reduce ST, and improve CV health. Finally, we used data from the DC CV Health and Needs Assessment to quantify the impact of crime on physical activity location accessibility, leisure-time physical activity (LTPA) and obesity among African-American women. We developed an agent-based model representing resource-limited Washington, DC communities and their populations to simulate the impact of crime on LTPA and obesity among African-American women under different circumstances. Our simulations show that reducing crime and increasing propensity to exercise through multilevel interventions (i.e. economic development initiatives to increase time available for physical activity and subsidized health care) may promote greater than linear declines in obesity prevalence. Crime prevention strategies alone can help prevent obesity, but combining such efforts with other ways to encourage physical activity can yield even greater benefits.